class_docstr: Computes LR as minimum of 1/beta and 1/(gamma * step) at each step.
  
  This is a required step for privacy guarantees.
docstr: Setups scheduler with beta and gamma values from the loss function.
  
  Meant to be used with .fit as the loss params may depend on values passed to
  fit.
  
  Args:
    beta: Smoothness value. See StrongConvexMixin
    gamma: Strong Convexity parameter. See StrongConvexMixin.
docstr_tok: ["Setups","scheduler","with","beta","and","gamma","values","from","the","loss","function",".","Meant","to","be","used","with",".fit","as","the","loss","params","may","depend","on","values","passed","to","fit",".","Args",":","beta",":","Smoothness","value",".","See","StrongConvexMixin","gamma",":","Strong","Convexity","parameter",".","See","StrongConvexMixin","."]
code: def initialize(self, beta, gamma):
      """Setups scheduler with beta and gamma values from the loss function.
  
      Meant to be used with .fit as the loss params may depend on values passed to
      fit.
  
      Args:
        beta: Smoothness value. See StrongConvexMixin
        gamma: Strong Convexity parameter. See StrongConvexMixin.
      """
      self.is_init = True
      self.beta = beta
      self.gamma = gamma
  
imports: import tensorflow.compat.v1
  from tensorflow.compat.v1.python.keras.optimizer_v2 import optimizer_v2
  from tensorflow.compat.v1.python.ops import math_ops
  from tensorflow_privacy.privacy.bolt_on.losses import StrongConvexMixin
label_desc: Use image node_id 4 for calling the GammaBetaDecreasingStep obj's underlying member method code with example usage: obj.initialize(beta, gamma) without return types